The fastest tactical way to launch this model locally is via a Docker image.
Follow the sequence of steps detailed below.
The installer automatically pulls the model (could be multiple GBs).
The setup file includes a feature that instantly optimizes all configurations.
📤 Release Hash: f84bf58ab08c5d9a6dadb553266ce957 • 📅 Date: 2026-07-01
- CPU: modern architecture (Zen 3 / Alder Lake minimum)
- RAM: 32 GB highly recommended for 26B+ GGUF models
- Storage:100 GB free space for HuggingFace cache folder
- GPU: high memory bandwidth GPU for next-gen local AI pipeline
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MiniMax-M2.7-NVFP4 is a highly optimized, 4-bit quantized variant of MiniMaxAI’s flagship 230-billion parameter sparse Mixture-of-Experts (MoE) foundation model, compressed via NVIDIA Model Optimizer using the cutting-edge NVFP4 (Nvidia Floating Point 4-bit) format. The architecture leverages a blockwise FP8 scaling scheme per 16 elements, dropping the previous Lightning Attention layers in favor of pure, hardware-optimized Grouped-Query Attention (GQA) with 48 query heads and 8 KV heads. This aggressive mathematical alignment allows the massive model to execute on a mere 10B active parameters per token, reducing VRAM demands dramatically down to 70 GB per GPU in Tensor Parallel setups. Tailored for self-evolving agent loops, multi-file code refactoring, and real-world system debugging, it delivers extreme processing throughput over an expansive 196,608-token context window while maintaining an exceptional 56.22% score on the SWE-Pro engineering benchmark.
| Specification |
Detail |
| Total / Active Parameters |
230 Billion Total / 10 Billion Active per Token (Sparse MoE) |
| Quantization Layout |
NVFP4 (4-bit Weights with Blockwise FP8 Scales via Nvidia Model Optimizer) |
| Context Window |
196,608 tokens (196k natively) |
| Hardware Baseline |
Dual NVIDIA RTX PRO 6000 Blackwell (96GB GDDR7) or H100 Tensor Parallel |
| Attention Mechanism |
Standard GQA Softmax (48 Query / 8 KV Heads) |
| Primary Execution Engines |
vLLM Native Server, SGLang Backend with b12x |
| Core Benchmarks |
SWE-Pro: 56.22% / Terminal Bench 2: 57.0% / VIBE-Pro: 55.6% |
- Setup tool configuring MemGPT local agents with Ollama backend links
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- Installer configuring privateGPT setups using modern hardware backends
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